The importance of implementation: Putting evaluation policy to work
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Federal agencies are increasingly expected to write and implement guidance for program evaluation, also known as evaluation policies. The Foundations for Evidence‐Based Policymaking Act required such policies for some federal agencies, and guidance from the White House Office of Management and Budget outlined an expectation that all agencies develop evaluation policies. Before these expectations, many federal agencies were already developing such policies to suit organizational needs and contexts. This chapter details findings from interviews with stakeholders at ten federal agencies and offices that developed and implemented evaluation policies before enacting the Foundations for Evidence‐Based Policymaking Act. These organizations represent early adopters of evaluation policies that can support future guidance and implementation of evaluation frameworks and capacity building in government. The study provides insight into the breadth and depth of the various strategies they used as well as their experiences with implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it